上传几个骨架提取的代码

 下面这个是骨架提取的代码实现

//两个方法吧，我记不得在哪看的了
enum
{
	THINNING_ZHANGSUEN,
	THINNING_GUOHALL
};

//骨架提取
void thinningIteration(cv::Mat img, int iter, int thinningType)
{
	cv::Mat marker = cv::Mat::zeros(img.size(), CV_8UC1);
	if (thinningType == THINNING_ZHANGSUEN) {
		for (int i = 1; i < img.rows - 1; i++)
		{
			for (int j = 1; j < img.cols - 1; j++)
			{
				uchar p2 = img.at<uchar>(i - 1, j);
				uchar p3 = img.at<uchar>(i - 1, j + 1);
				uchar p4 = img.at<uchar>(i, j + 1);
				uchar p5 = img.at<uchar>(i + 1, j + 1);
				uchar p6 = img.at<uchar>(i + 1, j);
				uchar p7 = img.at<uchar>(i + 1, j - 1);
				uchar p8 = img.at<uchar>(i, j - 1);
				uchar p9 = img.at<uchar>(i - 1, j - 1);

				int A = (p2 == 0 && p3 == 1) + (p3 == 0 && p4 == 1) +
					(p4 == 0 && p5 == 1) + (p5 == 0 && p6 == 1) +
					(p6 == 0 && p7 == 1) + (p7 == 0 && p8 == 1) +
					(p8 == 0 && p9 == 1) + (p9 == 0 && p2 == 1);
				int B = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
				int m1 = iter == 0 ? (p2 * p4 * p6) : (p2 * p4 * p8);
				int m2 = iter == 0 ? (p4 * p6 * p8) : (p2 * p6 * p8);

				if (A == 1 && (B >= 2 && B <= 6) && m1 == 0 && m2 == 0)
					marker.at<uchar>(i, j) = 1;
			}
		}
	}
	if (thinningType == THINNING_GUOHALL) {
		for (int i = 1; i < img.rows - 1; i++)
		{
			for (int j = 1; j < img.cols - 1; j++)
			{
				uchar p2 = img.at<uchar>(i - 1, j);
				uchar p3 = img.at<uchar>(i - 1, j + 1);
				uchar p4 = img.at<uchar>(i, j + 1);
				uchar p5 = img.at<uchar>(i + 1, j + 1);
				uchar p6 = img.at<uchar>(i + 1, j);
				uchar p7 = img.at<uchar>(i + 1, j - 1);
				uchar p8 = img.at<uchar>(i, j - 1);
				uchar p9 = img.at<uchar>(i - 1, j - 1);

				int C = ((!p2) & (p3 | p4)) + ((!p4) & (p5 | p6)) +
					((!p6) & (p7 | p8)) + ((!p8) & (p9 | p2));
				int N1 = (p9 | p2) + (p3 | p4) + (p5 | p6) + (p7 | p8);
				int N2 = (p2 | p3) + (p4 | p5) + (p6 | p7) + (p8 | p9);
				int N = N1 < N2 ? N1 : N2;
				int m = iter == 0 ? ((p6 | p7 | (!p9)) & p8) : ((p2 | p3 | (!p5)) & p4);

				if ((C == 1) && ((N >= 2) && ((N <= 3)) & (m == 0)))
					marker.at<uchar>(i, j) = 1;
			}
		}
	}
	img &= ~marker;
}

// 这个是调用的方法，输入的图像需要是二值化后的图像
void thinning(cv::Mat input, cv::Mat& output, int thinningType)
{
	output = input.clone();
	// Enforce the range of the input image to be in between 0 - 255
	output /= 255;
	cv::Mat prev = cv::Mat::zeros(output.size(), CV_8UC1);
	cv::Mat diff;

	do {
		thinningIteration(output, 0, thinningType);
		thinningIteration(output, 1, thinningType);
		absdiff(output, prev, diff);
		output.copyTo(prev);
	} while (countNonZero(diff) > 0);
	output *= 255;
}

下面是提取最大的轮廓点，并且去除短的分支和自相交的轮廓点

int findLongContours(Mat img,vector<cv::Point2i> &contoursLong)
{
	img /= 255;
	for (int i = 2; i < img.rows-2;i++)
	{
		uchar* ptr0 = img.ptr<uchar>(i - 1);
		uchar* ptr1 = img.ptr<uchar>(i);
		uchar* ptr2 = img.ptr<uchar>(i + 1);
		for (int j = 2; j < img.cols-2; j++)
		{
			if (ptr1[j]==1)
			{
				int hopCount = abs(ptr0[j] - ptr0[j-1]) + abs(ptr0[j+1] - ptr0[j])
					         + abs(ptr1[j+1] - ptr0[j+1]) + abs(ptr2[j+1] - ptr1[j+1])
				             + abs(ptr2[j] - ptr2[j+1]) + abs(ptr2[j-1] - ptr2[j])
					         + abs(ptr1[j-1] - ptr2[j-1]) + abs(ptr0[j-1] - ptr1[j-1]);
				if (hopCount>=5)
				{
					ptr0[j - 1] = 0, ptr0[j] = 0, ptr0[j + 1] = 0;
					ptr1[j - 1] = 0, ptr1[j] = 0, ptr1[j + 1] = 0;
					ptr2[j - 1] = 0, ptr2[j] = 0, ptr2[j + 1] = 0;
					vector<vector<cv::Point2i>> countours;
					findContours(img, countours, cv::RETR_LIST, cv::CHAIN_APPROX_NONE);
					vector<Point2i> vecSize;
					for (int m = 0; m < countours.size();m++)
					{
						vecSize.push_back(cv::Point2i(m,countours[m].size()));
					}
					sort(vecSize.begin(), vecSize.end(), comparePoint2iY);
					img = Mat::zeros(img.size(), CV_8UC1);
					if (vecSize.size() <= 2)  //去除自交的轮廓点，有优化空间，去判断自交的轮廓
					{
						cv::polylines(img, countours[vecSize[0].x], false, 1);
						continue;
					}
					cv::polylines(img, countours[vecSize[0].x], false, 1);
					cv::polylines(img, countours[vecSize[1].x], false, 1);
					//补齐断点
					ptr1[j] = 1;
					uchar* ptrCur = img.data;
					for (int m = i - 2; m < i + 3;m++)
					{
						for (int n = j - 2; n < j + 3;n++)
						{
							if (ptrCur[m*img.cols + n] != 0)
							{
								int xClose = (n+j)/2;
								int yClose = (m + i) / 2;
								ptrCur[yClose*img.cols + xClose] = 1;
							}
						}
					}

				}
			}
		}
	}
	vector<vector<cv::Point2i>> contoursOut;
	findContours(img, contoursOut, cv::RETR_TREE, cv::CHAIN_APPROX_NONE);
	for (int i = 0; i < contoursOut.size();i++)
	{
		for (int j = 0; j < contoursOut[i].size();j++)
		{
			contoursLong.push_back(contoursOut[i][j]);
		}
	}

	return 1;
}

效果如下：

二值化图像

 提取骨架的图像

 抽出最大的轮廓，去除分支点和自相交的分支点（对于后面拟合圆，拟合直线能够去除很大的干扰）